Evaluation of the Risk of Urinary System Stone Recurrence Using Anthropometric Measurements and Lifestyle Behaviors in a Developed Artificial Intelligence Model

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dc.contributor.authorYasar, Hikmet
dc.contributor.authorYildirim, Kadir
dc.contributor.authorKaraduman, Mucahit
dc.contributor.authorKolcu, Bayram
dc.contributor.authorEzer, Mehmet
dc.contributor.authorSuceken, Ferhat Yakup
dc.contributor.authorSarica, Kemal
dc.date.accessioned2026-06-19T06:37:47Z
dc.date.available2026-06-19T06:37:47Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs.
dc.identifier.doi10.3390/diagnostics15202643
dc.identifier.issn2075-4418
dc.identifier.issue20
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.orcid0000-0002-8087-4044
dc.identifier.orcid0000-0001-7605-4353
dc.identifier.orcid0000-0001-7175-713X
dc.identifier.pmid41153315
dc.identifier.scopus2-s2.0-105020288171
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15202643
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5220
dc.identifier.volume15
dc.identifier.wosWOS:001602718600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectAutoencoder
dc.subjectArtificial Intelligence
dc.subjectClinical Decision Support System
dc.subjectStone Recurrence
dc.subjectUrinary System Stone Disease
dc.titleEvaluation of the Risk of Urinary System Stone Recurrence Using Anthropometric Measurements and Lifestyle Behaviors in a Developed Artificial Intelligence Model
dc.typeArticle

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